EM Can Find Pretty Good HMM POS-Taggers (When Given a Good Start)
Yoav Goldberg, Meni Adler, Michael Elhadad · 2008
We address the task of unsupervised POS tag-ging. We demonstrate that good results can be obtained using the robust EM-HMM learner when provided with good initial conditions, even with incomplete dictionaries. We present a family of algorithms to compute effective initial estimations p(t|w). We test the method on the task of full morphological disambigua-tion in Hebrew achieving an error reduction of 25 % over a strong uniform distribution base-line. We also test the same method on the stan-dard WSJ unsupervised POS tagging task and obtain results competitive with recent state-of-the-art methods, while using simple and effi-cient learning methods. 1